LLM Fairness Dashboard

Bank Complaint Handling Fairness Analysis

Generated: 2025-09-20T14:38:29.714877 | Total Experiments: 1,000

0.131
Zero-Shot Accuracy
0.215
N-Shot Accuracy
1,000
Sample Size

Persona Injection

Result 1: Does Persona Injection Affect Tier?
[Placeholder: Analysis of tier assignment differences between persona-injected and baseline experiments]
Result 2: Does Persona Injection Affect Process?
[Placeholder: Analysis of process discrimination differences between persona-injected and baseline experiments]
Result 3: Does Gender Injection Affect Tier?

Hypothesis: The mean tier is the same with and without gender injection

Test: Paired t-test

Mean Difference: [MEAN_DIFFERENCE]

Test Statistic: t([DEGREES_OF_FREEDOM]) = [TEST_STATISTIC]

p-value: [P_VALUE]

Result 4: Does Ethnicity Injection Affect Tier?
[Placeholder: Ethnicity-specific tier assignment bias analysis]
Result 5: Does Geography Injection Affect Tier?
[Placeholder: Geography-specific tier assignment bias analysis]
Result 6: Top 3 Advantaged and Disadvantaged Personas
[Placeholder: Ranking of personas by advantage/disadvantage in tier assignments]
Result 7: Does Persona Injection Affect Accuracy?
[Placeholder: Impact of persona injection on prediction accuracy]
Result 8: Does Zero-Shot Prompting Amplify Bias?
[Placeholder: Comparison of bias levels between zero-shot and n-shot approaches]

Severity and Bias

Result 1: Does Severity Affect Tier Bias?
[Placeholder: Analysis of how complaint severity influences tier assignment bias]
Result 2: Does Severity Affect Process Bias?
[Placeholder: Analysis of how complaint severity influences process discrimination]

Bias Mitigation

Result 1: Can Bias Mitigation Reduce Tier Bias?
[Placeholder: Effectiveness of bias mitigation strategies on tier assignment bias]
Result 2: Can Bias Mitigation Reduce Process Bias?
[Placeholder: Effectiveness of bias mitigation strategies on process discrimination]
Result 3: Most and Least Effective Bias Mitigation Strategies
[Placeholder: Ranking of bias mitigation strategies by effectiveness]
Result 4: Does Bias Mitigation Affect Accuracy?
[Placeholder: Impact of bias mitigation on prediction accuracy]

Ground Truth Accuracy

Result 1: Does N-Shot Prompting Improve Accuracy?
[Placeholder: Comparison of zero-shot vs n-shot accuracy performance]
Result 2: Most and Least Effective N-Shot Strategies
[Placeholder: Ranking of n-shot strategies by accuracy performance]

Tier Recommendations

Result 1: Confusion Matrix – Zero Shot
Persona Tier
Baseline12
015711
18,341851
24542,186
Result 2: Confusion Matrix – N-Shot
Persona Tier
Baseline012
06031,1232
13947,529357
253401,647
Result 3: Tier Impact Rate
LLM Method Same Tier Different Tier Total % Different
n shot 9,779 2,221 12,000 18.5%
zero shot 10,527 1,473 12,000 12.3%
Total 20,306 3,694 24,000 15.4%

Conclusion:

H0: persona-injection does not affect tier selection

Conclusion: The null hypothesis is rejected.

Implication: The LLM is influenced by sensitive personal attributes.

Result 4: Mean Tier – Persona-Injected vs. Baseline
LLM Method Mean Baseline Tier Mean Persona Tier N Std Dev SEM
n shot 1.02 1.08 12,000 0.43 0.0039
zero shot 1.21 1.25 12,000 0.35 0.0032

Statistical Analysis (N Shot):

H0: The mean tier is the same with and without persona injection

Test: Paired t-test

Effect Size (Cohen's d): 0.14 (negligible)

Mean Difference: +0.06 (from 1.02 to 1.08)

Test Statistic: t(11999) = 15.7892

p-value: < 0.0001

Conclusion: The null hypothesis is rejected (p < 0.05).

Implication: The LLM's recommended tier is higher when it sees humanizing attributes, somewhat analogous to a display of empathy.

Statistical Analysis (Zero Shot):

H0: The mean tier is the same with and without persona injection

Test: Paired t-test

Effect Size (Cohen's d): 0.14 (negligible)

Mean Difference: +0.05 (from 1.21 to 1.25)

Test Statistic: t(11999) = 14.9801

p-value: < 0.0001

Conclusion: The null hypothesis is rejected (p < 0.05).

Implication: The LLM's recommended tier is higher when it sees humanizing attributes, somewhat analogous to a display of empathy.

Result 5: Tier Distribution – Persona-Injected vs. Baseline
MethodTier 0Tier 1Tier 2
Baseline79728193
Persona Injected1,00217,9445,054

Statistical Analysis:

H0: The tier distribution is independent of persona injection.

Test: Chi-squared test of independence

Test Statistic: χ²(2) = 32.72

p-value: < 0.0001

Conclusion: The null hypothesis is rejected (p < 0.05).

Implication: The distributions of tier recommendations are significantly different, suggesting that persona injection influences the pattern of tier assignments.

Process Bias

Result 1: Question Rate – Persona-Injected vs. Baseline – Zero-Shot
Condition Count Questions Question Rate %
Baseline 500 29 5.8%
Persona-Injected 12,000 542 4.5%

Statistical Analysis:

H0: The question rate is the same with and without persona injection

Test: Chi-squared test of independence

Test Statistic: χ²(1) = 1.53

p-value: 0.2160

Conclusion: The null hypothesis is not rejected (p ≥ 0.05).

Implication: The LLM's question rate is not significantly affected by humanizing attributes.

Result 2: Question Rate – Persona-Injected vs. Baseline – N-Shot
Condition Count Questions Question Rate %
Baseline 500 0 0.0%
Persona-Injected 12,000 24 0.2%

Statistical Analysis:

H0: The question rate is the same with and without persona injection

Test: Chi-squared test of independence

Test Statistic: χ²(1) = 0.23

p-value: 0.6315

Conclusion: The null hypothesis is not rejected (p ≥ 0.05).

Implication: The LLM's question rate is not significantly affected by humanizing attributes.

Result 3: N-Shot versus Zero-Shot
Method Count Questions Question Rate %
Zero-Shot 12,000 542 4.5%
N-Shot 12,000 24 0.2%

Statistical Analysis:

H0: The question rate is the same with and without N-Shot examples

Test: Chi-squared test of independence

Test Statistic: χ²(1) = 483.65

p-value: < 0.0001

Conclusion: The null hypothesis is rejected (p < 0.05).

Implication: N-Shot examples reduce the influence of sensitive personal attributes on the LLM's questioning behavior.

Gender Bias

Result 1: Mean Tier by Gender and by Zero-Shot/N-Shot

Zero-Shot Mean Tier by Gender

Gender Mean Tier Count Std Dev
Female 1.266 6,000 0.442
Male 1.242 6,000 0.429

Statistical Analysis - Zero-Shot

Hypothesis: H0: Persona injection does not affect mean tier assignment

Test: Paired t-test

Effect Size (Cohen's d): 0.053

Mean Difference: 0.023

Test Statistic: t(11998) = 2.895

p-value: 0.0038

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: The LLM's mean recommended tier is biased by gender, disadvantaging males.

N-Shot Mean Tier by Gender

Gender Mean Tier Count Std Dev
Female 1.067 6,000 0.508
Male 1.101 6,000 0.478

Statistical Analysis - N-Shot

Hypothesis: H0: Persona injection does not affect mean tier assignment

Test: Paired t-test

Effect Size (Cohen's d): -0.070

Mean Difference: -0.034

Test Statistic: t(11998) = -3.812

p-value: 0.0001

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: The LLM's mean recommended tier is biased by gender, disadvantaging females.

Result 2: Tier Distribution by Gender and by Zero-Shot/N-Shot

Zero-Shot Tier Distribution by Gender

GenderTier 1Tier 2
Female4,4071,593
Male4,5451,455

Statistical Analysis - Zero-Shot

Hypothesis: H0: Persona injection does not affect the distribution of tier assignments

Test: Chi-squared test

Test Statistic: χ²(1) = 8.254

p-value: 0.0041

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: The LLM's recommended tiers are biased by gender.

N-Shot Tier Distribution by Gender

GenderTier 0Tier 1Tier 2
Female5884,425987
Male4144,5671,019

Statistical Analysis - N-Shot

Hypothesis: H0: Persona injection does not affect the distribution of tier assignments

Test: Chi-squared test

Test Statistic: χ²(2) = 32.968

p-value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: The LLM's recommended tiers are biased by gender.

Result 3: Tier Bias Distribution by Gender and by Zero-Shot/N-Shot
Gender Count Mean Zero-Shot Tier Mean N-Shot Tier
Female 12,000 1.266 1.067
Male 12,000 1.242 1.101

Statistical Analysis

Hypothesis: H0: For each gender, the within-case expected decision is the same for zero-shot and n-shot

Test: cumulative-logit (proportional-odds) mixed model with random intercept for case_id

Test Statistic: F = 276.041

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: The LLM's recommended tiers are biased by an interaction of gender and LLM prompt.

Result 4: Question Rate – Persona-Injected vs. Baseline – by Gender and by Zero-Shot/N-Shot

Zero-Shot Question Rate by Gender

Gender Count Questions Question Rate %
Female 6,000 290 4.8%
Male 6,000 252 4.2%

Statistical Analysis - Zero-Shot

Hypothesis: H0: The question rate is the same across genders

Test: Chi-squared test of independence

Rate Difference: 0.6%

Test Statistic: χ²(1) = 2.645

p-value: 0.1039

Conclusion: The null hypothesis was accepted (p ≥ 0.05)

Implication: There is no evidence that the LLM's questioning behavior is biased by gender.

N-Shot Question Rate by Gender

Gender Count Questions Question Rate %
Female 6,000 13 0.2%
Male 6,000 11 0.2%

Statistical Analysis - N-Shot

Hypothesis: H0: The question rate is the same across genders

Test: Chi-squared test of independence

Rate Difference: 0.0%

Test Statistic: χ²(1) = 0.042

p-value: 0.8381

Conclusion: The null hypothesis was accepted (p ≥ 0.05)

Implication: There is no evidence that the LLM's questioning behavior is biased by gender.

Result 5: Disadvantage Ranking by Gender and by Zero-Shot/N-Shot
Ranking Zero-Shot N-Shot
Most Advantaged Female Male
Most Disadvantaged Male Female

Note: Rankings are based on mean tier assignments. Higher mean tiers indicate more advantaged outcomes.

Ethnicity Bias

Result 1: Mean Tier by Ethnicity and by Zero-Shot/N-Shot

Zero-Shot Mean Tier by Ethnicity

EthnicityMean TierCountStd Dev
Asian 1.277 3,000 0.448
Black 1.264 3,000 0.441
Latino 1.240 3,000 0.427
White 1.235 3,000 0.424

Statistical Analysis

Hypothesis: H0: The mean tier is the same across all ethnicities

Test: One-way ANOVA

Comparison: All ethnicities: asian, black, latino, white

Test Statistic: F = 6.502

p-Value: 0.0002

Effect Size (η²): 0.002

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the LLM's recommended tiers differ significantly between ethnicities in Zero-Shot. Means: asian=1.277, black=1.264, latino=1.240, white=1.235

N-Shot Mean Tier by Ethnicity

EthnicityMean TierCountStd Dev
Asian 1.088 3,000 0.496
Black 1.085 3,000 0.488
Latino 1.091 3,000 0.483
White 1.071 3,000 0.507

Statistical Analysis

Hypothesis: H0: The mean tier is the same across all ethnicities

Test: One-way ANOVA

Comparison: All ethnicities: asian, black, latino, white

Test Statistic: F = 0.944

p-Value: 0.4184

Effect Size (η²): 0.000

Conclusion: The null hypothesis was accepted (p ≥ 0.05)

Implication: There is no evidence that the LLM's recommended tiers differ between ethnicities in N-Shot. Means: asian=1.088, black=1.085, latino=1.091, white=1.071

Result 2: Tier Distribution by Ethnicity and by Zero-Shot/N-Shot

Zero-Shot Tier Distribution by Ethnicity

EthnicityTier 1Tier 2
Asian2,168832
Black2,207793
Latino2,281719
White2,296704

Statistical Analysis

Hypothesis: H0: The tier distribution is the same across ethnicities

Test: Chi-squared test of independence

Test Statistic: χ² = 19.481

Degrees of Freedom: 3

p-Value: 0.0002

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the tier distribution differs significantly between ethnicities in Zero-Shot.

N-Shot Tier Distribution by Ethnicity

EthnicityTier 0Tier 1Tier 2
Asian2492,238513
Black2412,263496
Latino2262,276498
White2862,215499

Statistical Analysis

Hypothesis: H0: The tier distribution is the same across ethnicities

Test: Chi-squared test of independence

Test Statistic: χ² = 9.135

Degrees of Freedom: 6

p-Value: 0.1661

Conclusion: The null hypothesis was accepted (p ≥ 0.05)

Implication: There is no evidence that the tier distribution differs between ethnicities in N-Shot.

Result 3: Tier Bias Distribution by Ethnicity and by Zero-Shot/N-Shot
EthnicityCountMean Zero-Shot TierMean N-Shot Tier
Asian 6,000 1.277 1.088
Black 6,000 1.264 1.085
Latino 6,000 1.240 1.091
White 6,000 1.235 1.071

Note: Mean tiers are calculated from persona-injected experiments only (excluding bias mitigation).

Statistical Analysis

Hypothesis: H0: For each ethnicity, the within-case expected decision is the same for zero-shot and n-shot

Test: cumulative-logit (proportional-odds) mixed model with random intercept for case_id

Test Statistic: F = 117.777

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the LLM's recommended tiers are biased by an interaction of ethnicity and LLM prompt.

Result 4: Question Rate – Persona-Injected vs. Baseline – by Ethnicity and by Zero-Shot/N-Shot

Zero-Shot Question Rate by Ethnicity

EthnicityQuestionsTotalQuestion Rate
Asian 142 3,000 4.7%
Black 149 3,000 5.0%
Latino 128 3,000 4.3%
White 123 3,000 4.1%

Statistical Analysis

Hypothesis: H0: The question rate is the same across ethnicities

Test: Chi-squared test of independence

Test Statistic: χ² = 3.378

Degrees of Freedom: 3

p-Value: 0.3370

Conclusion: The null hypothesis was accepted (p ≥ 0.05)

Implication: There is no evidence that the question rate differs between ethnicities in Zero-Shot.

N-Shot Question Rate by Ethnicity

EthnicityQuestionsTotalQuestion Rate
Asian 8 3,000 0.3%
Black 6 3,000 0.2%
Latino 5 3,000 0.2%
White 5 3,000 0.2%

Statistical Analysis

Hypothesis: H0: The question rate is the same across ethnicities

Test: Chi-squared test of independence

Test Statistic: χ² = 1.002

Degrees of Freedom: 3

p-Value: 0.8008

Conclusion: The null hypothesis was accepted (p ≥ 0.05)

Implication: There is no evidence that the question rate differs between ethnicities in N-Shot.

Result 5: Disadvantage Ranking by Ethnicity and by Zero-Shot/N-Shot
Ranking Zero-Shot N-Shot
Most Advantaged Asian Latino
Most Disadvantaged White White

Note: Rankings are based on mean tier assignments. Higher mean tiers indicate more advantaged outcomes.

Geographic Bias

Result 1: Mean Tier by Geography and by Zero-Shot/N-Shot

Zero-Shot Mean Tier by Geography

GeographyMean TierCountStd Dev
Rural 1.165 4,000 0.371
Urban Affluent 1.285 4,000 0.452
Urban Poor 1.312 4,000 0.463

Statistical Analysis

Hypothesis: H0: The mean tier is the same across all geographies

Test: One-way ANOVA

Comparison: All geographies: rural, urban_affluent, urban_poor

Test Statistic: F = 131.922

p-Value: 0.0000

Effect Size (η²): 0.022

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the LLM's recommended tiers differ significantly between geographies in Zero-Shot. Means: rural=1.165, urban_affluent=1.285, urban_poor=1.312

N-Shot Mean Tier by Geography

GeographyMean TierCountStd Dev
Rural 1.044 4,000 0.508
Urban Affluent 1.079 4,000 0.505
Urban Poor 1.127 4,000 0.464

Statistical Analysis

Hypothesis: H0: The mean tier is the same across all geographies

Test: One-way ANOVA

Comparison: All geographies: rural, urban_affluent, urban_poor

Test Statistic: F = 28.615

p-Value: 0.0000

Effect Size (η²): 0.005

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the LLM's recommended tiers differ significantly between geographies in N-Shot. Means: rural=1.044, urban_affluent=1.079, urban_poor=1.127

Result 2: Tier Distribution by Geography and by Zero-Shot/N-Shot

Zero-Shot Tier Distribution by Geography

GeographyTier 1Tier 2
Rural3,340660
Urban Affluent2,8591,141
Urban Poor2,7531,247

Statistical Analysis

Hypothesis: H0: The tier distribution is the same across geographies

Test: Chi-squared test of independence

Test Statistic: χ² = 258.230

Degrees of Freedom: 2

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the tier distribution differs significantly between geographies in Zero-Shot.

N-Shot Tier Distribution by Geography

GeographyTier 0Tier 1Tier 2
Rural4312,961608
Urban Affluent3632,956681
Urban Poor2083,075717

Statistical Analysis

Hypothesis: H0: The tier distribution is the same across geographies

Test: Chi-squared test of independence

Test Statistic: χ² = 90.470

Degrees of Freedom: 4

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the tier distribution differs significantly between geographies in N-Shot.

Result 3: Tier Bias Distribution by Geography and by Zero-Shot/N-Shot
GeographyCountMean Zero-Shot TierMean N-Shot Tier
Rural 8,000 1.165 1.044
Urban Affluent 8,000 1.285 1.079
Urban Poor 8,000 1.312 1.127

Note: Mean tiers are calculated from persona-injected experiments only (excluding bias mitigation).

Statistical Analysis

Hypothesis: H0: For each geography, the within-case expected decision is the same for zero-shot and n-shot

Test: cumulative-logit (proportional-odds) mixed model with random intercept for case_id

Test Statistic: F = 221.384

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the LLM's recommended tiers are biased by an interaction of geography and LLM prompt.

Result 4: Question Rate – Persona-Injected vs. Baseline – by Geography and by Zero-Shot/N-Shot

Zero-Shot Question Rate by Geography

GeographyQuestionsTotalQuestion Rate
Rural 212 4,000 5.3%
Urban Affluent 114 4,000 2.9%
Urban Poor 216 4,000 5.4%

Statistical Analysis

Hypothesis: H0: The question rate is the same across geographies

Test: Chi-squared test of independence

Test Statistic: χ² = 38.692

Degrees of Freedom: 2

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: There is strong evidence that the question rate differs significantly between geographies in Zero-Shot.

N-Shot Question Rate by Geography

GeographyQuestionsTotalQuestion Rate
Rural 10 4,000 0.2%
Urban Affluent 4 4,000 0.1%
Urban Poor 10 4,000 0.2%

Statistical Analysis

Hypothesis: H0: The question rate is the same across geographies

Test: Chi-squared test of independence

Test Statistic: χ² = 3.006

Degrees of Freedom: 2

p-Value: 0.2225

Conclusion: The null hypothesis was accepted (p ≥ 0.05)

Implication: There is no evidence that the question rate differs between geographies in N-Shot.

Result 5: Disadvantage Ranking by Geography and by Zero-Shot/N-Shot
Ranking Zero-Shot N-Shot
Most Advantaged Urban Poor Urban Poor
Most Disadvantaged Rural Rural

Note: Rankings are based on mean tier assignments. Higher mean tiers indicate more advantaged outcomes.

Tier Recommendations

Analysis of tier recommendations by complaint severity (Monetary vs Non-Monetary cases).

Result 1: Tier Impact Rate – Zero Shot

Zero-Shot Tier Impact by Severity

Severity Category Count Average Tier Std Dev SEM Unchanged Count Unchanged %
Non-Monetary 9,360 1.092 0.289 0.003 8,341 89.1%
Monetary 2,640 1.828 0.377 0.007 2,186 82.8%

Statistical Analysis - Zero-Shot

Hypothesis: H0: Persona-injection biases the tier recommendation equally for monetary versus non-monetary cases

Test: Chi-squared test for independence (approximation of McNemar's test)

Test Statistic: χ² = 75.560

p-value: 0.000

Conclusion: The null hypothesis is rejected (p < 0.05)

Implication: There is strong evidence that bias is greater for more severe cases.

Result 2: Tier Impact Rate – N-Shot

N-Shot Tier Impact by Severity

Severity Category Count Average Tier Std Dev SEM Unchanged Count Unchanged %
Non-Monetary 10,008 0.936 0.363 0.004 8,132 81.3%
Monetary 1,992 1.824 0.387 0.009 1,647 82.7%

Statistical Analysis - N-Shot

Hypothesis: H0: Persona-injection biases the tier recommendation equally for monetary versus non-monetary cases

Test: Chi-squared test for independence (approximation of McNemar's test)

Test Statistic: χ² = 2.145

p-value: 0.143

Conclusion: The null hypothesis is accepted (p ≥ 0.05)

Implication: There is no evidence that bias differs between monetary and non-monetary cases.

Process Bias

Analysis of process bias (question rates) by complaint severity (Monetary vs Non-Monetary cases).

Result 1: Question Rate – Monetary vs. Non-Monetary – Zero-Shot

Zero-Shot Question Rates by Severity

Severity Category Count Baseline Question Count Baseline Question Rate % Persona-Injected Question Count Persona-Injected Question Rate %
Non-Monetary 9,750 29 7.4% 537 5.7%
Monetary 2,750 0 0.0% 5 0.2%

Statistical Analysis - Zero-Shot

Hypothesis: H0: Severity has no marginal effect upon question rates

Test: Chi-squared test for independence (approximation of GEE)

Test Statistic: χ² = 158.080

p-value: 0.000

Conclusion: The null hypothesis is rejected (p < 0.05)

Implication: There is strong evidence that severity has an effect upon process bias via question rates.

Note: Full GEE implementation would cluster by case_id and use robust Wald tests

Result 2: Question Rate – Monetary vs. Non-Monetary – N-Shot

N-Shot Question Rates by Severity

Severity Category Count Baseline Question Count Baseline Question Rate % Persona-Injected Question Count Persona-Injected Question Rate %
Non-Monetary 10,425 0 0.0% 13 0.1%
Monetary 2,075 0 0.0% 11 0.6%

Statistical Analysis - N-Shot

Hypothesis: H0: Severity has no marginal effect upon question rates

Test: Chi-squared test for independence (approximation of GEE)

Test Statistic: χ² = 16.464

p-value: 0.001

Conclusion: The null hypothesis is rejected (p < 0.05)

Implication: There is strong evidence that severity has an effect upon process bias via question rates.

Note: Full GEE implementation would cluster by case_id and use robust Wald tests

Tier Recommendations

Analysis of how bias mitigation strategies affect tier recommendations in LLM decision-making.

Result: Confusion Matrix – With Mitigation - Zero-Shot
Baseline Tier Mitigation Tier 0Mitigation Tier 1Mitigation Tier 2
Tier 0251,05596
Tier 11653,04111,287
Tier 202,25416,226
Result: Confusion Matrix – With Mitigation - N-Shot
Baseline Tier Mitigation Tier 0Mitigation Tier 1Mitigation Tier 2
Tier 04,5997,47522
Tier 13,53151,9442,485
Tier 21412,26511,538
Result: Tier Impact Rate – With and Without Mitigation
Decision Method Persona Matches Persona Non-Matches Persona Tier Changed % Mitigation Matches Mitigation Non-Matches Mitigation Tier Changed %
n-shot 68,453 15,547 18.5% 68,081 15,919 19.0%
zero-shot 73,689 10,311 12.3% 69,292 14,708 17.5%

Statistical Analysis

Hypothesis: H0: Bias mitigation has no effect on tier selection bias

Test: Chi-squared test for independence (approximation)

Test Statistic: χ² = 1713.3218653497565

p-value: 0.0

Conclusion: The null hypothesis was rejected (p 0.000)

Implication: There is strong evidence that bias mitigation affects tier selection bias.

Result: Bias Mitigation Rankings - Zero-Shot
Risk Mitigation Strategy Sample Size Mean Baseline Mean Persona Mean Mitigation Residual Bias % Std Dev SEM
Roleplay 12,000 1.206 1.254 1.193 26.2% 0.396 0.004
Persona Fairness 12,000 1.206 1.254 1.184 46.0% 0.393 0.004
Consequentialist 12,000 1.206 1.254 1.279 152.4% 0.449 0.004
Minimal 12,000 1.206 1.254 1.308 213.2% 0.462 0.004
Chain Of Thought 12,000 1.206 1.254 1.395 393.9% 0.490 0.004
Perspective 12,000 1.206 1.254 1.400 404.9% 0.490 0.004
Structured Extraction 12,000 1.206 1.254 1.537 689.8% 0.499 0.005

Statistical Analysis - Zero-Shot

Hypothesis: H0: All bias mitigation methods are just as effective (or ineffective) as one another

Model: Linear Mixed-Effects Model (subject-specific interpretation) - Model: bias ~ mitigation + persona [+ mitigation:persona] + (1 | case_id)

Test: Likelihood-ratio test comparing models with vs without the mitigation term (approximated by repeated-measures ANOVA)

Test Statistic: F = 28.123031129676164

p-value: 5.468554308092003e-33

Effect Size (η²): 0.04608144497805653

Conclusion: The null hypothesis was rejected (p 0.000)

Implication: There is strong evidence that bias mitigation strategies differ in effectiveness.

Note: Analysis based on Linear Mixed-Effects Model with case_id as random effect. Full implementation would use specialized mixed-effects libraries.

Result: Bias Mitigation Rankings - N-Shot
Risk Mitigation Strategy Sample Size Mean Baseline Mean Persona Mean Mitigation Residual Bias % Std Dev SEM
Structured Extraction 12,000 1.022 1.084 1.024 3.4% 0.531 0.005
Chain Of Thought 12,000 1.022 1.084 1.038 25.4% 0.541 0.005
Minimal 12,000 1.022 1.084 1.040 29.2% 0.527 0.005
Perspective 12,000 1.022 1.084 1.080 94.3% 0.492 0.004
Persona Fairness 12,000 1.022 1.084 1.093 115.3% 0.524 0.005
Consequentialist 12,000 1.022 1.084 1.101 127.3% 0.468 0.004
Roleplay 12,000 1.022 1.084 1.106 135.7% 0.481 0.004

Statistical Analysis - N-Shot

Hypothesis: H0: All bias mitigation methods are just as effective (or ineffective) as one another

Model: Linear Mixed-Effects Model (subject-specific interpretation) - Model: bias ~ mitigation + persona [+ mitigation:persona] + (1 | case_id)

Test: Likelihood-ratio test comparing models with vs without the mitigation term (approximated by repeated-measures ANOVA)

Test Statistic: F = 0.5334435497035303

p-value: 0.7832257677785995

Effect Size (η²): 0.0009154684208064133

Conclusion: The null hypothesis was accepted (p 0.783)

Implication: There is no evidence that bias mitigation strategies differ in effectiveness.

Note: Analysis based on Linear Mixed-Effects Model with case_id as random effect. Full implementation would use specialized mixed-effects libraries.

Process Bias

Result 1: Question Rate – With and Without Mitigation – Zero-Shot
[Placeholder: Information request rates with/without mitigation in zero-shot experiments]
Result 2: Question Rate – With and Without Mitigation – N-Shot
[Placeholder: Information request rates with/without mitigation in n-shot experiments]
Result 3: Implied Stereotyping - Monetary vs. Non-Monetary
[Placeholder: Stereotyping analysis with bias mitigation effects]
Result 4: Bias Mitigation Rankings
[Placeholder: Process bias mitigation strategy rankings]

Accuracy Analysis

Result 1: Overall Accuracy Comparison
Ground Truth \ LLM Tier 0 Tier 1 Tier 2
Tier 0 7 322 86
Tier 1 0 49 8
Tier 2 0 12 16
Result 2: Zero-Shot vs N-Shot Accuracy Rates
Decision Method Experiment Category Sample Size Correct Accuracy %
n-shot Baseline 500 125 25%
n-shot Bias Mitigation 84,000 18,213 22%
n-shot Persona-Injected 12,000 2,453 20%
zero-shot Baseline 500 72 14%
zero-shot Bias Mitigation 84,000 10,960 13%
zero-shot Persona-Injected 12,000 1,644 14%
Note: Ground truth accuracy metrics are based on comparison with manually verified complaint resolution tiers. Accuracy measurements help validate the effectiveness of different fairness approaches while maintaining predictive performance.

Method Comparison

Result 1: Zero-Shot vs N-Shot Performance
[Placeholder: Detailed comparison of zero-shot and n-shot accuracy across different conditions]
Result 2: Baseline vs Persona-Injected Accuracy
[Placeholder: Impact of persona injection on prediction accuracy]
Result 3: With vs Without Bias Mitigation
[Placeholder: Accuracy performance with and without bias mitigation strategies]

Strategy Analysis

Result 1: Most and Least Effective Strategies
[Placeholder: Ranking of all experimental approaches by accuracy performance]
Result 2: Accuracy by Bias Mitigation Strategy
[Placeholder: Accuracy performance for different bias mitigation approaches]
Result 3: N-Shot Strategy Effectiveness
[Placeholder: Comparison of different n-shot prompting strategies]